System and method for diagnosing pathologic heart conditions

ABSTRACT

A method of diagnosing pathologic heart conditions in which a time series of heart sounds is filtered and parsed into a sequence of individual heart cycles. A systolic interval as well as systolic sub-intervals are identified for each heart cycle. An energy value is computed for the systolic sub-interval of one or more heart cycles. The energy value computed is proportional to the energy level associated with the filtered series of heart sounds. A composite energy value is then computed for the systolic sub-intervals of one or more heart cycles and compared to a threshold level in order to distinguish between a normal heart and a pathologic heart. The system corresponding to the method is comprised of a portable computing device that manages data collection and stores data collected from new patients, and analyzes data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of International Application No.PCT/US01/06016, filed Feb. 23, 2001 which claims the benefit of priorfiled co-pending U.S. Provisional Patent Application No. 60/184,375,filed on Feb. 23, 2000.

STATEMENT OF GOVERNMENT INTEREST

This invention was made with Government support under Contract No.DAMD17-97-7016 awarded by the Department of the Army. The Government hascertain rights in the invention.

BACKGROUND OF THE INVENTION

The present invention relates to a system and method for diagnosingpathologic heart conditions based upon heart sound data.

Studies have shown that primary care physicians frequently referpatients to cardiac specialists on the basis of suspicious heart soundsdetected by traditional stethoscope auscultation, though a largepercentage of these referrals are dismissed by cardiologists as havingno pathologic condition. The costs, delays, worry, and administrativeburden resulting from these needless referrals could be reduced if thecues that the specialist uses could be incorporated into an algorithm toautomatically screen for pathologic heart sounds and murmurs. Althoughattempts have been made to automate screening by auscultation, no deviceis currently available to fulfill this function. Multiple indicators ofpathology are nonetheless available from heart sounds and can beelicited using certain signal processing techniques such astime-frequency analysis. At least one signal of pathology, the systolicmurmur, can reliably be detected and classified as pathologic using aportable electrocardiogram and heart sound measurement unit combinedwith a time-frequency based algorithm. Time-frequency decompositionanalysis holds promise for extending these results to detection andevaluation of other audible pathologic indicators.

In addition, an automatic screening algorithm would be useful fordetecting pathologic heart murmurs in settings where a trainedprofessional is not always available, such as pre sports participationphysicals, and examinations performed in remote or underserved areas.Furthermore, automated analysis of digitized clinical information suchas heart sounds could have major implications for health care deliverysystems using telemedicine.

SUMMARY OF THE INVENTION

The present invention comprises a time-frequency murmur diagnosticdevice and method. The present invention combines a cardiologist'sauscultation expertise, a large and growing set of comprehensive heartsound files, and digital signal processing algorithms.

A method of diagnosing pathologic heart conditions in which a timeseries of heart sounds is filtered and parsed into a sequence ofindividual heart cycles. A systolic interval as well as systolicsub-intervals are identified for each heart cycle. An energy value iscomputed for the systolic sub-interval of one or more heart cycles. Theenergy value computed is proportional to the energy level associatedwith the filtered series of heart sounds. A composite energy value isthen computed for the systolic sub-intervals of one or more heart cyclesand compared to a threshold level in order to distinguish between a,normal heart and a pathologic heart.

The system for diagnosing pathologic heart conditions is comprised of aportable computing device that manages data collection and stores datacollected from new patients, and analyzes data. Also included is apatient data collection unit, communicable with the portable computingdevice, for acquiring and digitizing electro-cardiogram (ECG) and heartsound data from a patient. The patient data collection unit is comprisedof a pair of transducer contact microphones (primary and reference) forobtaining acoustic data. Also included is a pair of acousticpre-amplifiers connected with the transducers. The pre-amplifiers have apassband of 20 Hz to 2 kHz and are used to condition acoustic datareceived from the contact microphones. Variable amplifiers connectedwith the acoustic pre-amplifiers variably amplify the conditionedacoustic data. Moreover, several electrocardiogram (ECG) electrodesconnected to an ECG amplifier record ECG data. The acoustic and the ECGdata are passed to an analog to digital converter connected with thevariable amplifiers and the ECG amplifier. The data is digitized andsent to the computing device for processing by a screening algorithmimplemented by the steps described in the method above.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a plot of time-frequency analysis basis functions(wavelets) at various scales α.

FIG. 2 illustrates a normal heart sound under time-frequency analysis.

FIG. 3 illustrates a pathologic systolic heart murmur undertime-frequency analysis.

FIG. 4 illustrates a block diagram of system hardware.

FIG. 5 illustrates a logic flow diagram of the processes used todiagnose pathologic heart conditions.

FIG. 6 illustrates a logic flow diagram for optimizing the parameters ofa time-frequency screening algorithm.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

Before describing the present invention it is helpful to have a basisfor the detection of pathological heart conditions. Analysis of avariety of heart sounds and corresponding diagnoses from the JohnsHopkins School of Medicine (JHU/SOM) has shown that time-frequencyanalysis is a versatile technique for detecting and classifyingpathologic heart conditions. Of the available time-frequency techniques,wavelets are a useful method for representing heart sound frequencydynamics without creating cross term artifacts. Wavelet transforms canbe computed for a continuous or discrete set of transform variables,depending on the priority for compactness (e.g., data compressionapplications) or ease of visual interpretation (e.g., patternrecognition applications), respectively. While compressibility is ofinterest for future applications, the relative ease of interpretationprovided by continuous wavelet transforms (CWTs) suggested their usewith the present invention. While wavelet transform analysis areillustrated herein as a preferred method for analyzing heart sound data,Fourier transform analysis may also be implemented by one of ordinaryskill in the art in order to analyze heart sound data.

The CWT of a time series, f(t), is defined as: $\begin{matrix}{{W\left( {a,b} \right)} \equiv {\int_{- \infty}^{\infty}{{f(t)}\frac{1}{\sqrt{|a|}}{\psi^{*}\left( \frac{t - b}{a} \right)}{\mathbb{d}t}}}} & \left( {{Equation}\quad 1} \right)\end{matrix}$where f and ψ are both square-integrable, a is a time scaling variable,and b is a time translation variable. This can also be written as aconvolution:W(a, b)=f(b)*ψ*_(a,0)(−b)where${\psi_{a,b}(t)} \equiv {\frac{1}{\sqrt{|a|}}\psi\left( \frac{t - b}{a} \right)}$

While it is possible to construct a ψ to yield an optimal peak (compact,high amplitude) in W for a given f, it would not be guaranteed to beoptimal for a different time series g. Since the present invention is tobe applied to a variety of heart sound signals, a custom wavelet was notimplemented. Rather, the alternative was to draw ψ from a pool ofwavelets designed to have various advantageous properties. Wavelettransformations known as second order “coiflets” were implemented withthe present invention. It is important to note, however, that otherwavelet transformations, including custom wavelets, may be implementedwithout departing from the spirit or scope of the present invention.

Coiflets of order 2 at various scales, α, are plotted in FIG. 1. Thefrequency bandpass limits at each scale are given. Due to theequivalence of convolution in the time domain and multiplication in thefrequency domain equation 1 shows that the Fourier transform of ψ willact as a bandpass filter of the signal f. The bandpass limits in FIG. 1are the 6 dB passband frequency limits of the Fourier transform of φ.Wavelets are constructed so as to maintain a constant ratio of centerfrequency to 3 dB bandwidth (Q), and have a finite duration. Theirtime-frequency resolution is inherent in their design and scaleparameters. This is in contrast to Fourier decomposition, which uses theinfinite time extent sine and cosine functions. Time resolution is notinherent in the Fourier transform, but is introduced by the user viawindowing the data. Multiple Fourier transforms using distinct windowintervals would be required to produce the constant Q decompositionoffered by wavelets.

Example cases of a normal heart sound and a pathologic systolic heartmurmur under wavelet transformation with coiflets are given in FIGS. 2and 3. These figures demonstrate the clear relationship between anaudio-based physician's description used in auscultation and the visualpresentation in time-frequency space. A “harsh pan-systolic murmur” is adiffuse area of broadscale (i.e., broadband) energy between S1 and S2.The broadband nature of the sound, which lasts throughout systole, isresponsible for “harshness.” This straightforward representation of apathologic indicator in time-scale space is a promising basis forpattern recognition.

Auscultation of pathologic murmurs is keyed to the followingobservations, according to a study of 222 consecutive patients referredto the Johns Hopkins Pediatric Cardiology clinic:

-   -   a. pan-systolic nature of the murmur;    -   b. intensity of the murmur>grade 3;    -   c. point of maximal murmur intensity at the left upper sternal        border (LUSB);    -   d. harsh quality of the murmur;    -   e. presence of an early or mid-systolic click; or, presence of        an abnormal second heart sound.

The goal was to identify systolic murmurs that are indicative of heartdefects, and exhibited one or more of the qualities (a)-(d) above.Algorithms may also be implemented to detect heart clicks, and split andabnormal S2 sounds for greater diagnostic utility.

A system block diagram of the present invention is illustrated in FIG.4. The system is comprised of two principal elements. One is a patientdata collection unit 401. The other is a computer processing device 420including or having access to data storage devices. The patient datacollection unit performs several functions including obtaining heartsound data via a set of contact microphones in the form of transducers.Two channels of acoustic data are obtained from a patient using aprimary transducer 402 and a reference transducer 404. In addition, aset of ECG electrodes 406 are used to obtain electrocardiogram data fromthe patient.

The two contact microphones are each conditioned by a pre-amplifier 408having a passband of 20 Hz to 2 kHz and variable gain amplificationstage 410. A set of headphones 412 connected to a headphone amplifier414 can be used to listen to the acoustic data gathered from the primarytransducer 402 and the reference transducer 404. The ECG electrodes 406feed into an ECG amplifier 416. Outputs from the variable gainamplifiers. 410 and the ECG amplifier 416 are fed to a analog-to-digitalconverter where the acoustic and ECG signals are digitized and recorded.A 12-bit National Instruments PCMCIA analog-to-digital converter isused, for instance, to collect and digitize data at a rate (e.g. 8.13kHz) consistent with the highest data frequencies of interest.

Once the signals have been digitized and recorded, the patient datacollection unit 401 forwards the data to a computer processing device420. The computer processing device 420 is typically, a laptop computer(due to its compact transportable nature) having adequate data storagecapacity. However, the patient data collection unit 401 may be connectedto other computer processing devices without departing from the spiritor scope of the present invention. A computer software program accessesheart sound data that has either been collected and forwarded by thepatient data collection unit 401, or is resident on the laptop computer420, or can be obtained from another source of heart sound data. Thecomputer program applies a screening algorithm to the heart sound datain order to determine whether the heart sound data is to be classifiedas normal or pathologic. Each heart sound data file corresponds to adifferent patient. When the algorithm has operated on the heart sounddata that has been input, the computer program will display the resultson a display screen to the doctor, nurse, or technician operating thecomputer. Results indicating a pathologic condition will likely causethe patient to be referred to a cardiologist for further examination.Otherwise, a cardiologist referral can be deemed unnecessary.

FIG. 5 illustrates the flow of logic and processing that occurs in thevarious elements described in FIG. 4. Digitized heart sound recordingswere collected on patients in the Pediatric Cardiology EchocardiographyLaboratory in the Johns Hopkins Outpatient Center. The recordings werestored in a Heart Sound database 502. Recorded heart sounds could thenbe extracted 504 from the heart sound database 502 and placed into anECG/heart sound data file set 508. Alternatively, ECG and heart sounddata could be obtained directly from a patient 506 and placed into theECG/heart sound data file set 508. The ECG/heart sound data file set 508serves as the data to be fed to a screening algorithm. The purpose ofthe screening algorithm is to analyze the ECG data and heart sound datain order to detect any pathologic anomalies that may be present. Thusthe system is to be used as a diagnostic aid. In order for the screeningalgorithm to be applied, the data set must first be manipulated.Initially, the ECG data and acoustic heart sound data are separated. TheECG data is used to parse the time series into a sequence of individualheart cycles via a process that identifies the ECG peaks 510.

A systolic interval is then identified 512 for each heart cycle. Thefirst and second heart sounds are identified either by reference to theheart cycle boundaries or acoustically using a passband of 25-140 Hz. Inthe acoustic method the times of the acoustic maxima define systole anddiastole. Systole can then be divided into various sub-intervalsincluding, but not limited to, the first half of the systolic interval,the total systolic interval, the middle half of the systolic interval,and the last half of the systolic interval, “beginning after S1 andending at S2, as shown in FIG. 3 for a pan-systolic murmer.” A shortinterval to isolate the first and second heart sounds is factored in.Meanwhile, the acoustic heart sound data is passed through a digitalbandpass filter 514, in this case a second order coiflet CWT transform.Next, a relative energy value (square of the wavelet coefficientexpressed in dB) for a given wavelet scale and systolic subinterval iscalculated 516.

A composite relative energy value across all included heart cycles iscomputed. The composite energy value can be computed in several manners.One way is to compute it as the median of the set of computed energyvalues for each systolic sub-interval of the included heart cycles. Asecond way is to compute it as the weighted average of the set ofcomputed energy values for each systolic sub-interval of the includedheart cycles. A third way is to compute it as the median relative energyacross more than one of the heart cycle systolic sub-intervals. A fourthway is to compute it as the weighted average relative energy valueacross more than one of the heart cycle systolic sub-intervals. Those ofordinary skill in the art could readily devise other alternative ways inwhich to compute a composite energy value without departing from thespirit or scope of the present invention. The specific methods describedherein are illustrative and not intended to limit the invention to aparticular manner for computing a composite energy value.

A decision 518 between healthy 520 and pathologic 522 hearts is made onthe basis of the calculated composite relative energy value being aboveor below a certain threshold. A patient's processed data is saved 524for optional further technical analysis, and added to a database 526.Among the various uses of the database 526 are reviewing and improvingthe algorithm's performance.

FIG. 6 illustrates a logic flow diagram for optimizing the parameters ofa time-frequency screening algorithm. The diagnostic system andprocesses described above were applied to a set of heart sounds. A setof known pathologic heart sounds and a set of known normal heart soundstaken in the first half of each month over a period of time wereextracted from the heart sound database 602. Files from the latter halfof the months were preserved as “new” data to test algorithm performanceafter algorithm tuning. Each heart sound data file corresponds to adifferent patient. The screening algorithm is applied 604 to a patient'sheart sound data file and the results are recorded 606. Performance ofthe screening algorithm over all tested patients for a given thresholdwas measured by the ratio of called positives to true positives, withina universe of known positives (the “sensitivity”), paired with the ratioof called negatives to true negatives, within a universe of knownnegatives (the “specificity”). Sensitivity vs. specificity curves for avariety of thresholds and systolic intervals were plotted 607. Thesecurves are called “Receiver Operating Characteristic” or “ROC” curves.

The test is then repeated 608 on the same heart sound data using adifferent set of wavelet parameters, specifically, the scale (α) and thesystolic sub-interval (SsI) are varied. This is done for numerouscombinations of scale and systolic sub-interval. Once the heart sounddata for all patients has been subjected to the screening algorithm andresults have been recorded for the numerous scale and systolicsub-interval variations, the program compares the ROC curves for eachwavelet scale/systolic sub-interval combination 610. The area beneatheach ROC curve is computed and the ROC curve having an area closest to“1” is deemed to have the best results. The parameters for that ROCcurve are then chosen as the optimal parameters to use with thescreening algorithm 612.

The ROC curves, in the test case, indicated that the most optimalalgorithm settings used a wavelet scale, α, of 16 and was applied to themiddle half systolic sub-interval. The best results were typicallyobtained using a systolic interval centered on systole meaning that themidpoint of the systolic interval and the midpoint of the systolicsub-interval are the same. Those of ordinary skill in the art couldreadily adjust the algorithm for different wavelet scales and systolicsub-intervals without departing from the spirit or scope of the presentinvention.

Using these optimum parameters, the algorithm was then applied to anexpanded data set that included both half months plus additional datacollected while the previously described analysis was in progress. Of143 cases tested, 95 were from normal hearts (with and without innocentmurmurs) and 48 were from hearts with pathology (i.e., murmur grade≧2).Sensitivity and specificity ratios of 96% were achieved.

There are several advantages realized by the present invention. Anautomatic screening algorithm would be useful for detecting pathologicheart murmurs in settings where a trained professional is not alwaysavailable, such as pre sports participation physicals, and examinationsperformed in remote or underserved areas. Furthermore, automatedanalysis of digitized clinical information such as heart sounds couldhave major implications for health care delivery systems usingtelemedicine.

Automated analysis of heart sound data could also be used bycardiologists to quantitatively follow and document changes in severityof certain conditions such as aortic stenosis and mitral regurgitation,in which changes in murmur characteristics are known to correlate withchanges in disease severity. In addition, since data for analysis couldtheoretically be collected using any electronic stethoscope, home carenurses or nurses aides could make inexpensive in-home bedside recordingsthat could be analyzed later to monitor changes in certain conditions asreflected in heart sounds.

It is to be understood that the present invention illustrated herein isreadily implementable by those of ordinary skill in the art as acomputer program product having a medium with computer program(s)embodied thereon. The computer program product is capable of beingloaded and executed on the appropriate computer processing device(s) inorder to carry out the method or process steps described. Appropriatecomputer program code in combination with hardware implements many ofthe elements of the present invention. This computer code is typicallystored on removable storage media. This removable storage mediaincludes, but is not limited to, a diskette, standard CD, pocket CD, zipdisk, or mini zip disk. Additionally, the computer program code can betransferred to the appropriate hardware over some type of data network.

The present invention has been described, in part, with reference toflowcharts or logic flow diagrams. It will be understood that each blockof the flowchart diagrams or logic flow diagrams, and combinations ofblocks in the flowchart diagrams or logic flow diagrams, can beimplemented by computer program instructions.

These computer program instructions may be loaded onto a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructionswhich execute on the computer or other programmable data processingapparatus create means for implementing the functions specified in theflowchart block or blocks or logic flow diagrams.

These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meanswhich implement the function specified in the flowchart blocks or logicflow diagrams. The computer program instructions may also be loaded ontoa computer or other programmable data processing apparatus to cause aseries of operational steps to be performed on the computer or otherprogrammable apparatus to produce a computer implemented process suchthat the instructions which execute on the computer or otherprogrammable apparatus provide steps for implementing the functionsspecified in the flowchart blocks or logic flow diagrams.

Accordingly, block(s) of flowchart diagrams and/or logic flow diagramssupport combinations of means for performing the specified functions,combinations of steps for performing the specified functions and programinstruction means for performing the specified functions. It will alsobe understood that each block of flowchart diagrams and/or logic flowdiagrams, and combinations of blocks in flowchart diagrams and/or logicflow diagrams can be implemented by special purpose hardware-basedcomputer systems that perform the specified functions or steps, orcombinations of special purpose hardware and computer instructions.

In the following claims, any means-plus-function clauses are intended tocover the structures described herein as performing the recited functionand not only structural equivalents but also equivalent structures.Therefore, it is to be understood that the foregoing is illustrative ofthe present invention and is not to be construed as limited to thespecific embodiments disclosed, and that modifications to the disclosedembodiments, as well as other embodiments, are intended to be includedwithin the scope of the appended claims. The invention is defined by thefollowing claims, with equivalents of the claims to be included therein.

1. A method of diagnosing pathologic heart conditions comprising:identifying a systolic sub-interval of a systolic interval for aplurality of heart cycles in a sequence of heart cycles; computing anenergy value for each systolic sub-interval; computing a compositeenergy value using the computed energy values for each systolicsub-interval; and comparing the composite energy value to a thresholdlevel in order to distinguish between a normal heart and a pathologicheart.
 2. A method of diagnosing pathologic heart conditions comprising:filtering a time series of heart sounds; parsing the time series ofheart sounds into a sequence of individual heart cycles; identifying asystolic interval for each heart cycle; identifying a systolicsub-interval of the systolic interval for each heart cycle; computing anenergy value for the systolic sub-interval of one or more heart cycles,said energy value being proportional to the energy level associated withthe filtered series of heart sounds; computing a composite energy valuefor the systolic sub-intervals of one or more heart cycles; andcomparing the composite energy value to a threshold level in order todistinguish between a normal heart and a pathologic heart.
 3. The methodof claim 2 wherein said parsing step uses electro-cardiogram (ECG) datain order to transform a time series of heart sounds into a sequence ofindividual heart cycles.
 4. The method of claim 2 wherein said parsingstep uses acoustic heart sounds obtained directly from a patient inorder to transform a time series of heart sounds into a sequence ofindividual heart cycles.
 5. The method of claim 2 wherein identifying asystolic interval for each heart cycle is achieved by identifying pulseson an electro-cardiogram (ECG).
 6. The method of claim 2 whereinidentifying a systolic interval for each heart cycle is achieved byacoustically locating a first and a second heart sound using a bandpassfilter, said bandpass filter applied to the time series of heart sounds.7. The method of claim 2 wherein filtering the time series of heartsounds is achieved using a bandpass filter.
 8. The method of claim 2wherein filtering the time series of heart sounds is achieved usingtime-frequency transforms.
 9. The method of claim 8 wherein thetime-frequency transform is a wavelet transform.
 10. The method of claim8 wherein the time-frequency transform is a Fourier transform.
 11. Themethod of claim 2 wherein the systolic sub-interval is centered insystole.
 12. The method of claim 2 wherein the systolic sub-interval iscentered in systole and is half of the systolic interval.
 13. The methodof claim 2 wherein the composite energy value is computed as the medianof the computed energy values for more than one of the systolicsub-intervals of the heart cycles.
 14. The method of claim 2 wherein thecomposite energy value is computed as the weighted average of more thanone of the computed energy values for the systolic sub-intervals of theheart cycles.
 15. The method of claim 2 wherein the composite energyvalue is computed as the median across more than one of the heart cyclesystolic sub-intervals of a quantity proportional to energy.
 16. Themethod of claim 2 wherein the composite energy value is computed as theweighted average energy value across more than one of the heart cyclesystolic sub-intervals.
 17. The method of claim 14 wherein the ratio ofenergies between systolic interval and diastolic interval are also usedto distinguish a normal heart from a pathologic heart by priorstatistical characterization of the ratio of energies between systolicinterval and diastolic interval for normal and pathologic hearts. 18.The method of claim 14 wherein the standard deviation of the energy in asystolic interval is also used to distinguish a normal heart from apathologic heart by prior statistical characterization of the standarddeviation of the energy in a systolic interval for normal and pathologichearts.
 19. A system for diagnosing pathologic heart conditionscomprising: a portable computing device for: managing data collectionfrom new patients; storing data; and analyzing data, and a patient datacollection unit for acquiring electro-cardiogram (ECG) and heart sounddata from a patient, said patient data collection unit operativelyconnected with said portable computing device, wherein the patient datacollection unit comprises: a contact microphone for obtaining acousticdata; an acoustic pre-amplifier operatively connected with said contactmicrophone, said pre-amplifier having a passband of 20 Hz to 2 kHz usedto condition acoustic data received from said contact microphone; avariable amplifier operatively connected with said acousticpre-amplifier for variably amplifying the conditioned acoustic data; anelectro-cardiogram (ECG) electrode; an ECG amplifier operativelyconnected with said electro-cardiogram (ECG) electrode; an analog todigital converter operatively connected with said variable amplifier andsaid ECG amplifier, said analog to digital converter for digitizingacoustic data and electro-cardiogram (ECG) data.
 20. A method ofoptimizing a heart auscultation screening algorithm comprising: applyinga heart auscultation screening time-frequency transform algorithm to aset of data, wherein: said algorithm includes wavelets and bandpassfilters; said data includes heart sounds known to be normal and heartsounds known to be pathologic; said heart sounds being characterized bya systolic interval; said systolic interval capable of being dividedinto systolic sub-intervals, recording the results of said heartauscultation screening algorithm for a variety of time-frequencytransform parameters and systolic sub-intervals; and determining anoptimal combination of wavelet scale parameter and systolic sub-intervalfor use with said heart auscultation screening wavelet algorithm basedon sensitivity and specificity measurements.
 21. A computer readablemedium whose contents cause a computer based system to determine patientheart pathology by: identifying a systolic sub-interval of a systolicinterval for a plurality of heart cycles in a sequence of heart cycles;computing an energy value for each systolic sub-interval; computing acomposite energy value using the computed energy values for eachsystolic sub-interval; and comparing the composite energy value to athreshold level in order to distinguish between a normal heart and apathologic heart.
 22. A computer readable medium whose contents cause acomputer based system to determine patient heart pathology by: filteringa time series of heart sounds; parsing the time series of heart soundsinto a sequence of individual heart cycles; identifying a systolicinterval for each heart cycle; identifying a systolic sub-interval ofthe systolic interval for each heart cycle; computing an energy valuefor the systolic sub-interval of one or more heart cycles, said energyvalue being proportional to the energy level associated with thefiltered series of heart sounds; computing a composite energy value forthe systolic sub-intervals of one or more heart cycles; and comparingthe composite energy value to a threshold level in order to distinguishbetween a normal heart and a pathologic heart.
 23. A computer readablemedium whose contents transform a computer based system into a heartpathology detection system, comprising: a patient data collectionsubsystem for acquiring electro-cardiogram (ECG) and heart sound datafrom a patient; a data management subsystem for managingelectro-cardiogram (ECG) and heart sound data; a data analysis subsystemfor processing and analyzing electro-cardiogram (ECG) and heart sounddata comprising: means for identifying a systolic sub-interval of asystolic interval for a plurality of heart cycles in a sequence of heartcycles; means for computing an energy value for each systolicsub-interval; means for computing a composite energy value using thecomputed energy values for each systolic sub-interval; and means forcomparing the composite energy value to a threshold level in order todistinguish between a normal heart and a pathologic heart; and a datastorage subsystem for storing processed electro-cardiogram (ECG) andheart sound data.